Agents Make All Custom Software Viable at AIE Europe
AI agents like OpenClaw turn uneconomic custom automations into reality, expanding software markets, boosting engineer demand, and enabling personal-to-enterprise scaling.
Agents Fill the Software Universe
Malte Ubl (Vercel CTO) frames AI agents as the bridge to untapped software potential. Traditional coding couldn't economically justify countless automations—business rules, if-statements, domain knowledge too bespoke or niche. Agents change this: a Venn diagram shows the full circle of 'software that should exist' now fillable, as agentic workflows handle variability without hardcoded logic.
This shifts economics: cheaper software production tests market elasticity, with no S-curve in sight. Companies pivot from SaaS buys to 'make' decisions—'SaaS apocalypse' in Silicon Valley—but demand for engineers rises as more software gets built faster. Ubl predicts juniors molded in AI will excel, while veterans adapt. Actionable archetype: start with low-hanging non-coding agents (e.g., chat SDKs hooking agents to Slack/Telegram via TypeScript bash interpreters for nanosec sandboxes) before complex coding ones.
"Agents are a new kind of software because there was always all this stuff we wanted to automate but not all of it was economically viable to do with traditional software but it is with agents." Ubl emphasizes APIs must evolve to 'AI-first' for agent consumption.
OpenClaw Ecosystem Powers Personal and Enterprise Agents
Peter Steinberger (OpenAI/OpenClaw) reports explosive growth: 'State of the Claw' highlights security bounty overload from AI-generated exploits. swyx interview reveals OpenAI's open-source stance, 'token maxing' (inflating inference to fake productivity), and 'taste' as the un-AI-able engineering moat—curated judgment on outputs.
Practical deployments proliferate:
- Vincent Koc (Comet ML): 'Dark factories' run 60+ parallel agents overnight for codebase refactoring.
- Radek Sienkiewicz (VelvetShark): Delegates personal life to OpenClaw via Obsidian notes, email, background tasks—keys to life handed over.
- Onur Solmaz: ACP (Agent-Client Protocol) standardizes interactions; disposable enterprise agents on Kubernetes for isolation.
- Fryderyk Wiatrowski (Viktor): Slack-native 'AI employee' with context across thousands of tools.
- Merve Noyan (Hugging Face): Local coding agents via HF Hub skills for model training.
Ryan Lopopolo (OpenAI) pushes 'code is free' mindset: systems thinking + delegation spawns parallel AI coding agents. Humans steer, agents execute—harness engineering.
"Token maxing" critiqued by Gergely Orosz/swyx: Big Tech engineers waste inference padding metrics, eroding real gains.
Beyond Language: Multimodal Advances and Secure Scaling
Raia Hadsell (Google DeepMind VP Research) expands DeepMind past LLMs: Gemini Embeddings 2 for superior representations; AI cyclone prediction models; Project Genie 3 for generative skills. Non-language apps prove AI's breadth.
Security and infra harden agents:
- Sally Ann O'Malley (Red Hat): Podman/Docker/K8s for isolation, state recovery, formal verification in Clawdbot memory architecture.
- Nick Taylor (Pomerium): Identity-aware proxy hardens OpenClaw; live-codes MCP server from Discord.
Sunil Pai (Cloudflare): 'Code Mode' executes JavaScript in V8 isolates, bypassing slow JSON tool calls—10x speed for agent actions.
Kitze (Sizzy) roasts productivity apps, demos OS generating UI on-demand via AI. Matt Pocock (AI Hero) insists DDD/TDD combat AI 'slop': structured domains/tests filter hallucinations.
Community context: AIE grew 900% in 3 years; UK gov invests in AI infra, per Lia McBride. Event signals Europe's AI surge.
"We are speedrunning what's really an experiment in economics of how elastic the software market is." Ubl's thesis underpins agent hype.
"State of the Claw": OpenClaw's growth draws AI-generated security bounties, forcing rapid hardening.
Key Takeaways
- Build agent archetypes today: Hook to chats (Slack/Telegram) with sandboxes; target low-hanging automations saving millions without process overhauls.
- Delegate via systems thinking: Humans steer high-level, parallel agents execute code—'code is free.'
- Secure OpenClaw deployments: Use Podman/K8s isolation, Pomerium proxies, ACP for standardized client-agent comms.
- Scale with 'dark factories': Run 60+ parallel agents overnight for refactors; make disposable on K8s.
- Personalize ruthlessly: Integrate Obsidian/email/tasks into OpenClaw for life automation.
- Avoid token maxing: Focus on taste—curated outputs—as moat; enforce DDD/TDD against slop.
- Upgrade tools: Adopt Code Mode (V8 JS execution), HF local agents, Gemini Embeddings for non-LLM tasks.
- Design AI-first APIs: Agents as primary users demand structured, predictable interfaces.
- Monitor economics: Track if cheaper software expands markets—demand for AI engineers follows.